Fine-Grained Image Classification Based on Multi-Scale Feature Fusion

Siyao Li; Yuhong Liu; Rongfen Zhang

doi:10.3788/LOP57.121002

[1] Tang C, Ling Y S, Yang H et al. Decision-level fusion tracking for infrared and visible spectra based on deep learning[J]. Laser & Optoelectronics Progress, 56, 071502(2019).

[2] Liu F, Lu L X, Huang G W et al. Landform image classification based on discrete cosine transformation and deep network[J]. Acta Optica Sinica, 38, 0620001(2018).

[3] Zhao X. Research of fine-grained image recognition and classification algorithm based on deep convolutional neural network[D]. Hefei: Anhui University(2018).

[4] Zhao Z Y, Cheng Y L, Shi X S et al. Terrain classification of LiDAR point cloud based on multi-scale features and PointNet[J]. Laser & Optoelectronics Progress, 56, 052804(2019).

[5] Xie L X, Tian Q, Hong R C et al. Hierarchical part matching for fine-grained visual categorization. [C]∥2013 IEEE International Conference on Computer Vision, December 1-8, 2013, Sydney, Australia. IEEE, 1641-1648(2013).

[6] Chai Y N, Lempitsky V, Zisserman A. Symbiotic segmentation and part localization for fine-grained categorization. [C]∥2013 IEEE International Conference on Computer Vision, December 1-8, 2013, Sydney, Australia. IEEE, 321-328(2013).

[7] Zhang X P, Xiong H K, Zhou W G et al. Picking deep filter responses for fine-grained image recognition. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. IEEE, 1134-1142(2016).

[8] Zhao B, Wu X, Feng J S et al. Diversified visual attention networks for fine-grained object classification[J]. IEEE Transactions on Multimedia, 19, 1245-1256(2017).

[9] Fu J L, Zheng H L, Mei T. Look closer to see better: recurrent attention convolutional neural network for fine-grained image recognition. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI. IEEE, 440-455(2017).

[10] Yuan Q D. The Research on fine-grained object classification algorithm based on depth learning[D]. Changsha: Changsha University of Science and Technology(2017).

[11] Zhang N, Donahue J, Girshick R et al[M]. Part-based R-CNNs for fine-grained category detection, 834-849(2014).

[12] Zhang N, Paluri M, Ranzato M et al. PANDA: pose aligned networks for deep attribute modeling. [C]∥2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA. IEEE, 1637-1644(2014).

[13] Xiao T J, Xu Y C, Yang K Y et al. The application of two-level attention models in deep convolutional neural network for fine-grained image classification. [C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. IEEE, 842-850(2015).

[14] Lin T Y. RoyChowdhury A, Maji S. Bilinear CNN models for fine-grained visual recognition. [C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015. Santiago, Chile. IEEE, 1449-1457(2015).

[15] Gui J S, Ma C F, Bao X A et al. Fine-grained image classification method for recurrent deep hybrid attention network[J]. Computer Engineering, 45, 205-209(2019).

[16] Girshick R, Donahue J, Darrell T et al. Rich feature hierarchies for accurate object detection and semantic segmentation. [C]∥2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA. IEEE, 580-587(2014).

[17] Liu W, Anguelov D, Erhan D et al[M]. SSD: single shot MultiBox detector, 21-37(2016).

[18] Lin T Y, Dollar P, Girshick R et al. Feature pyramid networks for object detection. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI. IEEE, 936-944(2017).